Nations worldwide are vigorously promoting policies to improve energy efficiency. The use of measurement and verification (M&V) procedures to quantify energy performance is an essential topic in this field. Currently, energy performance M&V is accomplished via a combination of short-term on-site measurements and engineering calculations. This requires extensive amounts of time and labor and can result in a discrepancy between actual energy savings and calculated results. In addition, the M&V period typically lasts for periods as long as several months or up to a year, the failure to immediately detect abnormal energy performance not only decreases energy performance, results in the inability to make timely correction, and misses the best opportunity to adjust or repair equipment and systems. In this study, a cloud computing platform for the real-time M&V of energy performance is developed. On this platform, particle swarm optimization and multivariate regression analysis are used to construct accurate baseline models. Instantaneous and automatic calculations of the energy performance and access to long-term, cumulative information about the energy performance are provided via a feature that allows direct uploads of the energy consumption data. Finally, the feasibility of this real-time M&V cloud platform is tested for a case study involving improvements to a cold storage system in a hypermarket. Cloud computing platform for real-time energy performance M&V is applicable to any industry and energy conservation measure. With the M&V cloud platform, real-time and long-term energy performances can be obtained. By tracking fluctuations in energy performance, real-time monitoring or correction of the operating performance of equipment or system can help to maintain good energy performance. Thus, real-time energy management can be accomplished based on the above attributes. In addition, the cloud computing platform developed in this research can improve our national M&V level. Specifically, it helps government in promoting energy efficiency programs and the development of energy service industries. (C) 2016 Elsevier Ltd. All rights reserved.